Topic: Machine Learning for Advanced Design and Applications of High-Performance Ceramics

High-performance ceramics possess excellent mechanical, thermal, and electrical properties, making them ideal candidates for numerous applications in the aerospace, automotive, electronics, and biomedical industries. In recent years, research into high-performance ceramics has intensified due to their potential in addressing critical technological demands, such as energy efficiency, environmental sustainability, and enhanced material performance. As the demand for advanced ceramic materials increases, innovative design and manufacturing processes become paramount.

Additionally, the concept of the high entropy has been successfully introduced to the design of high-performance ceramics. By incorporating multiple principal elements, the design space and properties of ceramic materials can be effectively tuned. The chemical complexity, on the one hand, brings about unusual performance to high-entropy ceramics but, at the same time, poses a great challenge for the rational design of ceramics within the immersed space.

Machine learning has emerged as a powerful tool for solving complex problems in materials science and engineering. Its ability to analyze large amounts of data, identify hidden patterns, and generate predictive models has demonstrated great potential in revolutionizing the way we design, develop, and characterize high-performance ceramics. By leveraging machine learning techniques, researchers can accelerate the discovery and optimization of novel ceramic materials, as well as enhance their understanding of the underlying mechanisms governing their properties.

This Special Issue aims to provide a comprehensive understanding of the most recent advancements, challenges, and future directions in the field of high-performance ceramics, with an emphasis on the integration of machine learning methodologies, which brings together a collection of original research articles, reviews, and perspectives from leading experts in the field of ceramics and machine learning. The topics covered in this issue include, but are not limited to:
1. Machine learning-based approaches for the design and optimization of high-performance ceramic materials;
2. Application of machine learning or machine learning-assisted simulations in predicting mechanical, thermal, and electrical properties of ceramics;
3. Data-driven modeling of composition-processing-structure-property relationships in ceramics;
4. Advanced machine learning techniques for characterizing the microstructure and defects in ceramic materials;
5. Machine learning-assisted process control and quality assurance in ceramic manufacturing;
6. Development of high throughput experimental/computational methods for the establishment of high-fidelity databases.

High entropy ceramics, Machine Learning, high-performance ceramics, high-throughput computation and experimentation, materials development, materials characterization, high-fidelity databases

20 May 2024